One common challenge with spreadsheets is properly handling missing data values. Assigning a zero value to missing data can distort calculations, so there needs to be another way to indicate a missing value. A value can be designated, but it must be used consistently and documented carefully so it can be used in later revisions. So although there are workarounds for missing data in a spreadsheet, they’re not necessarily simple.
Increasingly, organizations are trying to use spreadsheets to determine what is likely to happen in the future. Some spreadsheets have dedicated forecast, trend and growth functions designed to predict new values based on existing data. However, the reliability and accuracy of these functions must be checked to verify the validity of results, including making sure the correct cells are being referenced.
Making a seemingly simple change to a spreadsheet, such as modifying times, adding new names or changing a formula can require dozens, even hundreds, of other changes that can be time-consuming and prone to errors. And adding new data to a spreadsheet can be equally risky. By automating data additions, data may be included in formulas when it shouldn’t be. By adding data manually, data may not be included where it needs to be.
Among logistical limitations with spreadsheets, there are also many errors that can occur.
One common challenge with spreadsheets is properly handling missing data values. Assigning a zero value to missing data can distort calculations, so there needs to be another way to indicate a missing value. A value can be designated, but it must be used consistently and documented carefully so it can be used in later revisions. So although there are workarounds for missing data in a spreadsheet, they’re not necessarily simple.
Increasingly, organizations are trying to use spreadsheets to determine what is likely to happen in the future. Some spreadsheets have dedicated forecast, trend and growth functions designed to predict new values based on existing data. However, the reliability and accuracy of these functions must be checked to verify the validity of results, including making sure the correct cells are being referenced.
Making a seemingly simple change to a spreadsheet, such as modifying times, adding new names or changing a formula can require dozens, even hundreds, of other changes that can be time-consuming and prone to errors. And adding new data to a spreadsheet can be equally risky. By automating data additions, data may be included in formulas when it shouldn’t be. By adding data manually, data may not be included where it needs to be.
Among logistical limitations with spreadsheets, there are also many errors
that can occur.
that can occur.
IBM SPSS Statistics software has functions specifically designed to appropriately handle missing data and dramatically reduce its effect on calculations. Whether that means imputing approximate data values based on existing data or excluding missing values from calculations, IBM SPSS Statistics software helps give you more-accurate results by properly handling missing data.
IBM SPSS Statistics software includes sophisticated time-series algorithms that help give you a more-accurate forecast. These special analytical techniques are designed to correctly handle seasonality, the impact of multiple variables and other factors that affect future projections.
IBM SPSS Statistics software separates data from results, so formulas, relationships and formats are preserved regardless of changes to data and vice versa. Every procedure that is run or output that is created generates an associated syntax, which can be rerun when new data is added or entered. This syntax facilitates consistency and accuracy within the results.
IBM SPSS Statistics software computes variables by referencing both metadata (a description of the data) and variables, whereas spreadsheets typically reference a cell. That means you can change the data, but the underlying metadata remains the same, helping reduce mistakes and improve accuracy.
With IBM SPSS Statistics software, you don’t need to write or copy and paste formulas—just select the ones you need from a menu. These built-in formulas are based on algorithms that have been tested and in use for more than 40 years.
Algorithms are embedded in IBM SPSS Statistics software, so you don’t need to worry about disrupting existing formulas in the cases and variables. The software separates data from results, so formulas, relationships and formats are preserved virtually regardless of changes to data and vice versa.
IBM SPSS Statistics software dramatically reduces misuse by offering a variety of case studies, a statistics coach and tutorial wizards designed to help you understand the right functions and procedures to use and when.
IBM SPSS Statistics software allows you to quickly search for anomalies within your data to determine whether numbers appear off and to detect where data may be incorrect or skewed.
IBM SPSS Statistics software creates metadata, or a description of the data, including labels, inputs and other relevant details. This means that, although you can change the data itself, the underlying metadata remains the same, which should result in fewer mistakes and better accuracy. Moreover, data validation techniques and procedures help you identify suspicious and invalid cases, variables, and data values.
Mistakes in logic tend to be simple, such as calling the wrong function, subtracting instead of adding or omitting parenthesis in formula creation. These sorts of errors can also be caused by the implied relationship of the cells in the spreadsheet.
Keying an equation into a cell while reading it from a piece of paper or another window can lead to errors. And copying existing equations and pasting them to new locations commonly changes the referenced cells in the formula.
If a cell contains an equation that looks like a number, a user may mistakenly insert a number into a cell containing a formula. That act will overwrite the equation and turn the contents of the cell into a constant. If other formulas rely on this cell, the error can compound significantly.
In spreadsheets, it’s possible and even quite easy to use the wrong function. For example, it would be easy to confuse the AVERAGEA function, which evaluates text and logical values, with the AVERAGE function, which ignores them.
It is easy to leave important information out of an equation, data or both. Errors of this type occur quite often when new data is added to a previously completed spreadsheet. It could be that not all the data is entered or some of the new cells aren’t included in all the relevant equations.
IBM SPSS Statistics software creates metadata, or a description of the data, including labels, inputs and other relevant details. This means that, although you can change the data itself, the underlying metadata remains the same, which should result in fewer mistakes and better accuracy. Moreover, data validation techniques and procedures help you identify suspicious and invalid cases, variables, and data values.
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